Accelerating High-Throughput Phonon Calculations via Machine Learning Universal Potentials

TL;DR

This paper introduces a machine learning approach using MACE potentials to accelerate harmonic phonon calculations across diverse materials, achieving high accuracy and reducing computational costs.

Contribution

The study develops a universal machine learning potential trained on extensive DFT data, enabling fast and accurate phonon property predictions for a wide range of materials.

Findings

Achieves MAE of 0.18 THz in phonon frequency predictions

Classifies dynamical stability with 86.2% accuracy

Demonstrates good agreement with DFT in thermodynamic stability analyses

Abstract

Phonons play a critical role in determining various material properties, but conventional methods for phonon calculations are computationally intensive, limiting their broad applicability. In this study, we present an approach to accelerate high-throughput harmonic phonon calculations using machine learning universal potentials. We train a state-of-the-art machine learning interatomic potential, based on multi-atomic cluster expansion (MACE), on a comprehensive dataset of 2,738 crystal structures with 77 elements, totaling 15,670 supercell structures, computed using high-fidelity density functional theory (DFT) calculations. Our approach significantly reduces the number of required supercells for phonon calculations while maintaining high accuracy in predicting harmonic phonon properties across diverse materials. The trained model is validated against phonon calculations for a held-out subset of 384 materials, achieving a mean absolute error (MAE) of 0.18 THz for vibrational frequencies from full phonon dispersions, 2.19 meV/atom for Helmholtz vibrational free energies at 300K, as well as a classification accuracy of 86.2% for dynamical stability of materials. A thermodynamic analysis of polymorphic stability in 126 systems demonstrates good agreement with DFT results at 300 K and 1000 K. In addition, the diverse and extensive high-quality DFT dataset curated in this study serves as a valuable resource for researchers to train and improve other machine learning interatomic potential models.